Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6’s object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system’s capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives.

Aim of this article was to describe the role of 3D technologies in education. Thus, possible to understand why these technologies will help to promote the level of knowledge, and why they are needed. Whether, there are schools that teach to the same technologies in Republic of Kazakhstan. What advantages of education of 3D technologies are in?

In this paper calculation and formation of aggregational values being the main elements of operative analysis of multidimensional data, in the form of hypercube and the development of effective correlation structure to the data during the process of the analytical analysis are considered. In the course of writing, basic concepts of multidimensional data model and internal structure are given. Measurements used for the implementation of hypercube and for the description of dimension elements the sequence of sets theory is applied. Pre-calculation of all aggregation values for quick operative analysis and in order to carry out the effective correlation to the values received in the result of calculation, multidimensional indexing are developed. In this structure, in accordance with the primary keys intersection of dimensional elements multidimensional indexing structure having been formed and pre-aggregation is made for the initial quantitative values through the formed multidimensional indexing structure. Pre-aggregation values in separate structure is stored and according to multidimensional indexing the pointer is installed to the aggregational value address. This means one multidimensional index matched with one value. After the termination of pre-aggregational values calculation, calculations of aggregational values that are solved in all the intersections of the values are considered. In the end of paper conclusion and literature are given.

AI is being deployed in smart-grid and smart-city energy infrastructure faster than governance frameworks can validate it. Existing instruments—NERC CIP, NIST AI RMF 1.0, and ISA/IEC 62443—address cybersecurity compliance and AI trustworthiness principles but do not provide operational control architecture for AI systems influencing grid stability, distributed energy resource (DER) dispatch, or real-time reliability decisions. Compliance with these instruments is not equivalent to operational assurance: their requirements do not by themselves establish validated input integrity controls, adversarial robustness testing, or bounded autonomy architecture for every AI-enabled operational use case. This paper presents the AI and Agentic Intelligence Governance Framework, Electric Sector (AAIGF-E): 111 controls across seven lifecycle phases (Govern, Design, Implement, Assure, Monitor, Respond, Recover), mapped to existing electric-sector compliance obligations. A defining structural feature of AAIGF-E is its dual-narrative control architecture, in which each control carries both an AI governance rationale and a parallel electric-sector operational translation, enabling the framework to serve AI governance stakeholders and operational technology (OT) engineering teams within the same control set without requiring cross-discipline translation. Scenario-driven validation tests the framework against three operationally relevant failure modes: adversarial manipulation of DER dispatch AI, unauthorized autonomous action execution by an agentic AI system in an OT environment, and silent model drift in grid stability forecasting. Validation confirms structural coverage across prevention, detection, and response phases and identifies residual implementation and assurance gaps while identifying a residual prompt-injection pathway in which technically valid operational content may manipulate agent task scope or apparent authority before downstream actuation controls activate. The 111-control register is further characterized through quantitative analysis of standards coverage, lifecycle and governance-domain distribution, cross-framework convergence, and sector-specific translation. A separate independent coding reproducibility check samples 35 controls across five reference frameworks, producing 175 binary mapping decisions; 155 decisions agree with the internal coding key (88.6% raw agreement; Cohen’s $\kappa =0.742$). To reduce reliance on author-led validation, a representative operational scenario is also evaluated by independent AI governance, cybersecurity, and ICS/OT experts who derive their judgments before reviewing the AAIGF-E reference assessment. Their domain selections and governance recommendations show strong overall convergence with the framework assessment. AAIGF-E is designed for electric utilities and smart-city operators within North American regulatory frameworks who need governance architecture that connects AI lifecycle risk management to existing compliance cycles rather than replacing them.

Credit scoring is fundamental in enabling institutions to distinguish between creditworthy and high-risk borrowers. Thus, robust credit scoring models are essential for effective financial risk management. Artificial intelligence and machine learning have significantly improved the precision of credit decision support systems, however, these models often operate as black boxes, concealing the underlying logic of credit denials or approvals. To address this challenge and meet the demand for financial transparency, explainable AI (XAI) has emerged as a crucial mechanism for generating decisions that are interpretable to both financial institutions and clients. This paper researched contemporary decision support systems (DSS) used in credit risk assessment and proposes a modular, plug-and-play framework with integrated explainability. Six requirements were derived for a robust system design based on a review of existing XAI frameworks. The framework consists of five interconnected components such as data ingestion layer, feature processing module, a supervised predictive model, an explanation layer, and a decision endpoint. Existing studies often focus on individual predictive models or explanation methods, leaving their integration and component interchangeability unspecified. The proposed framework addresses this gap through explicit interfaces that allow predictive models and explanation methods to be replaced independently. The system will operate with both traditional credit data, alternative data, or both. Thus, feature processing is incorporated to distinguish the importance and relevance of the data to credit assessment decisions. Any irrelevant redundant data is not be considered by the model. The contribution of a study is a reusable architectural specification. The proposed XAI framework is designed to serve as a universal template for researchers and financial institutions developing transparent, effective credit scoring systems.